Artificial Intelligence

    

Predictive Signals Obtained from Bayesian Network and the Prediction Quality

Authors: Ait-Taleb Nabil

In this paper, we will propose a method for learning signals related to a data frame $D_{1}$. The learning algorithm will be based on the biggest entropy variations of a Bayesian network. The method will make it possible to obtain an optimal Bayesian network having a high likelihood with respect to signals $D_{1}$. From the learned optimal Bayesian network, we will show what to do to infer new signals $D_{2}$ and we will also introduce the prediction quality $Delta_{CR}$ allowing to evaluate the predictive quality of inferred signals $D_{2}$. We will then infer a large number (10000) of candidate signals $D_{2}$ and we will select the predictive signals $D_{2}^{*}$ having the best prediction quality. Once the optimal signals $D_{2}^{*}$ obtained, we will impose the same order of scatter (computed from the Mahalanobis) to the points of signals $D_{2}^{*}$ as of signals $D_{1}$.

Comments: 14 Pages.

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Submission history

[v1] 2022-09-11 16:50:18
[v2] 2022-11-10 18:09:21
[v3] 2022-11-17 03:10:13

Unique-IP document downloads: 546 times

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